RNA sequencing

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.title[
# RNA sequencing
]
.author[
### <b>James Ashmore</b> • 24-Sep-2022
]
.institute[
### Zifo RnD Solutions
]

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# Introduction

## Overview of RNA-seq

* Gene expression studies get a snapshot of the RNA molecules present in a biological system

* Gene expression dictates what cells are doing or what cells are capable of doing

* A basic overview of the main steps in a standard RNA-seq experiment is given below

<br>

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-protocol.png" alt="A typical RNA-seq experiment" width="75%" />
<p class="caption">A typical RNA-seq experiment</p>
</div>

???

* The first step is the extraction and purification of RNA from a sample, followed by an enrichment of target RNAs

* Most commonly used is poly(A) capture, to select for polyadenylated RNAs

* Or ribosomal depletion, to deplete ribosomal RNAs that are highly abundant in a cell

* The selected RNAs are then chemically or enzymatically fragmented to molecules of approproiate size (e.g., 300 - 500 bp)

* Single-stranded target RNAs are reverse-transcribed to cDNA, the RNA is then degraded, and the cDNA is complemented to a double strand

* Adapter sequences are either ligated to the 3' and 5' end of the double-stranded cDNA or used as primers in the reverse transcription reaction

* The final cDNA library consists of cDNA inserts flanked by an adapter sequence on each end

* In the last step, the cDNA library is amplified by polymerase chain reaction (PCR) using parts of the adapter sequences as primers

---

# Design Aspects of RNA-seq

* Specific aspects to be considered while designing an RNA-seq experiment include:

* The number of replicates
  
      * Three is the minimum required to do any statistical analysis
  
  * The depth of sequencing

* In many genomic experiments resources are scarce (e.g., material from subjects)

* The first driver of sample size is often budget

---

# RNA-seq Applications

* The popularity of RNA-seq is driven by its large number of applications

* One of the main application areas is gene regulation:

* Comparison of gene expression between different tissues, cell types, genotypes, stimulation conditions, time points, disease states, growth condtions, and so on
  
  * The goal of such comparisons is to identify the genes that change in expression to understand the molecular pathways that are used or altered

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# ALIGNMENT AND QUANTIFICATION

## Introduction

* After an experiment has been conducted, the analyst is presented with FASTQ files

* Following sufficient quality control, the next step will either be:

1. Alignment to a reference genome
  
  2. Alignment to a reference transcriptome
  
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-alignment.png" alt="An illustration of spliced alignment of RNA-seq fragments" width="60%" />
<p class="caption">An illustration of spliced alignment of RNA-seq fragments</p>
</div>

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# Alignment and Quantification

## Spliced alignment to a reference Genome

* A popular solution for handling RNA-seq alignments is to use a splice-aware aligner

* Popular splice-aware aligners include [STAR](https://github.com/alexdobin/STAR) and [HISAT](http://daehwankimlab.github.io/hisat2/)

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/genome-alignment.png" alt="Spliced alignment of RNA-seq fragments to a genome" width="80%" />
<p class="caption">Spliced alignment of RNA-seq fragments to a genome</p>
</div>

---

# Alignment and Quantification

## Unspliced alignment to a reference transcriptome

* An alternative to splice-aware genome alignment is direct transcriptome alignment

* Direct transcriptome alignment consists of aligning against a set of known transcripts

* Popular transcriptome aligners include [Kallisto](https://pachterlab.github.io/kallisto/) and  [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html)

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/transcriptome-alignment.png" alt="Unspliced alignment of RNA-seq fragments to a transcriptome" width="50%" />
<p class="caption">Unspliced alignment of RNA-seq fragments to a transcriptome</p>
</div>

---

# Alignment and Quantification

## Gene- and Transcript-Level Quantification From RNA-seq Data

* One of the main uses of RNA-seq is to assess gene- and transcript-level abundances

* Most commonly, abundances are estimated at the level of genes

* Transcript-level abundances have become more widely used

---

# Alignment and Quantification

## Gene- and Transcript-Level Quantification From RNA-seq Data

* Gene-level quantification consists of assigning reads to genes

* A gene consists of all transcripts produced from a specific strand at a specific locus

* The total expression of a gene is the sum of the expression of its isoforms

* Popular stand-alone read counting tools include [featureCounts](http://subread.sourceforge.net) and [HTSeq](https://htseq.readthedocs.io/en/master/)

---

# Alignment and Quantification

## Transcript Quantification

* Gene-level quantification consists of assigning reads to genes

* A gene consists of all transcripts produced from a specific strand at a specific locus

* The total expression of a gene is the sum of the expression of its isoforms

* Popular stand-alone read counting tools include [featureCounts](http://subread.sourceforge.net) and [HTSeq](https://htseq.readthedocs.io/en/master/)

---

# Differential expression

## Overview

* Following alignment and quantification, the next step is testing for differential expression (DE)

* The starting point for DE is often a count table:

* Rows represent genomic features (e.g., genes)
  * Columns represent samples (i.e., experimental units)
  
* The goal of DE is to identify genes which are differentially expressed between conditions

---

# Differential expression

## Workflow

.pull-left-50[

* A

* B

* C

]

.pull-right-50[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/de-analysis-overview.png" alt="Schematic of a DE analysis for RNA-seq data" width="100%" />
<p class="caption">Schematic of a DE analysis for RNA-seq data</p>
</div>

]

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# Differential expression

## Filtering

* Genes with very low counts across all libraries provide little evidence for DE

* From a biological point of view:

* a gene must be expressed at some minimal level before it is likely to be translated into a protein or to be biologically important

* From a statistical point of view:

* The more inferences are made, the more likely erroneous inferences become
  
  *

* The expression level is indistinguishable from technical noise

* These genes should be filtered out prior to further analysis:

* As a rule of thumb, genes are dropped if they can’t possibly be expressed in all the samples for any of the conditions

* Users can set their own definition of genes being expressed

* Usually a gene is required to have a count of 5-10 in a library to be considered expressed in that library

---

# Differential expression

## Normalization

* The observed counts of the genes cannot be directly compared across samples since there
are differences in sequencing depth across libraries

* Several methods have been developed to normalize counts to facilitate cross-sample comparisons:

---

# Differential expression

## Modeling and estimation

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-experiment.png" alt="A typical RNA-seq experiment" width="60%" />
<p class="caption">A typical RNA-seq experiment</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-analysis-roadmap.png" alt="A generic roadmap for RNA-seq computational analyses" width="2596" />
<p class="caption">A generic roadmap for RNA-seq computational analyses</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-analysis-strategies.png" alt="Read mapping and transcript identification strategies" width="1891" />
<p class="caption">Read mapping and transcript identification strategies</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-common-tools.png" alt="Common software tools in use for differential gene expression analysis using RNA-seq data" width="2835" />
<p class="caption">Common software tools in use for differential gene expression analysis using RNA-seq data</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-sequencing-technology.png" alt="An overview is shown of the three main sequencing technologies for RNA-seq" width="100%" />
<p class="caption">An overview is shown of the three main sequencing technologies for RNA-seq</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-analysis-technology.png" alt="Comparison of short-read, long-read and direct RNA-seq analysis" width="85%" />
<p class="caption">Comparison of short-read, long-read and direct RNA-seq analysis</p>
</div>

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# RNA-seq analysis

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#data/rnaseq/images/rnaseq-analysis-workflow.png" alt="RNA-seq data analysis workflow for differential gene expression" width="100%" />
<p class="caption">RNA-seq data analysis workflow for differential gene expression</p>
</div>

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# Thank you. Questions?

.end-text[
<p class="smaller">
<span class="small" style="line-height: 1.2;">Graphics from </span><img src="./assets/freepik.jpg" style="max-height:20px; vertical-align:middle;"><br>
Created: 24-Sep-2022 • James Ashmore • <a href="https://www.zifornd.com/category/omics-bioinformatics">Bioinformatics</a> • <a href="https://www.zifornd.com">Zifo</a>
</p>
]